Question.4099 - Assignment Three: Bivariate analysis of gender and income Name: Part I: Use APA style and formatting for all assignments, references, and citations. Yes, have a cover page, too, as well as a running head. Try Purdue Owl for an example APA style paper: https://owl.english.purdue.edu/owl/resource/560/18/ Bivariate analysis Remember our hypothesis? We think there is a relationship between income and sex. Lets state our hypothesis in a way similar to how your text states a hypothesis. If someone is a woman, then their income will be lower on average than if they are a man. Using the ACS 2018-2022 dataset, recode pincp into a new variable called inccat using the following income categories. Input labels as shown in chapter 7. Make sure to save your file with the new variable, inccat. See below for an example of how the inccat should be coded. You enter the values one-by-one. I also make available for you a step-by-step guide in the weeks module. For a step-by-step guide of recoding and labeling variables see course materials. Make sure to save your file with the new variable or you will have to recode again later. First. once you create your inccat variable, go to the variable view screen and click into the values column for inccat. Second, click on measures and select ordinal. 44539298414722962275828040 Once you click into the value box for inccat the following box pops up: Now input the following information for the value labels areas before clicking on add. Value=1, Label= Less than $25,000 Value=2, Label= $25,000 to $49,999 Value=3, Label= $50,000 to $74,999 Value=4, Label= $75,000 to $99,999 Value=5, Label= $100,000 to $124,999 Value=6, Label= $125,000 to $149,999 Value=7, Label= $150,000 or more Once these labels are inputted, then click OK, and save your file, too. What kind of measure is your new, recoded inccat variable (nominal, ordinal, interval/ratio)? How come it might be more useful to use the recoded inccat variable than the pincp variable for our cross tabulation? Hint: Run a cross tabulation for the pincp variable and sex and then answer. Follow the directions in chapter 10 under demonstration 10.1 to run a bivariate analysis of inccat and sex. Copy and paste the tables below. Make sure you turn on your weight variable like you did last week. The weight variable is Pwgtp. Calculate and report Epsilon. See page 173 in your text regarding how to calculate Epsilon. Less than $25,000 Epsilon = $25,000 to $49,999 Epsilon = $50,000 to $74,999 Epsilon = $75,000 to $99,999 Epsilon = $100,000 to $124,999 Epsilon = $125,000 to $149,999 Epsilon = $150,000 or more Epsilon = Is there anything that stands out to you about the values of Epsilon? What about the cross tab? Would you say there is an association between inccat and sex based on Epsilon? Why or why not? Be thoughtful here and use information from your text and readings etc to answer questions like this. Based on Epsilon, how strong is the association if there is one? Cite your text to support your answer. What might your conclusions about inccat and sex be after running this basic cross tabulation and calculating Epsilon? Is our hypothesis, if someone is a woman, then their income will be lower than if they are a man, supported by your cross tabulation and Epsilon? Do not just answer yes or no here. Support your answer with numbers. (You could even look back at info from last week about pincp to help you think through the income categories you are looking at this week). Bonus: Is there anything you can say about the income categories? Could they be coded differently? How do we know which income categories to use? What are some other variables besides sex do you think might be important to analyze when seeking to explain income variations? Use your good thinking here.
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